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Application of Multi-sensor Information Fusion Based on Improved Particle Swarm Optimization in Unmanned System Path Planning

机译:基于改进粒子群算法的多传感器信息融合在无人系统路径规划中的应用

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Intelligent vehicle driving performance is safe and stable, which can significantly improve the efficiency of road traffic and reduce energy consumption, and intelligent vehicle is also the development direction of modern transport. Its core technology is intelligent environment perception module, by using a variety of sensors on the car in which the surrounding environment for data collection, processing module to provide effective control for the basis. In this paper, a new SINS / CNS / GPS integrated navigation observation equation is proposed, and a new federated data fusion structure is designed for the integrated navigation system. The particle filter is used to fuse the multi-source data of the federated filter subsystem, thus eliminating the limitations of the classical Kalman filter. The traditional Kalman filter structure and the federal particle filter mechanism are designed. The comparison shows that the proposed algorithm is effective in the information fusion of the integrated navigation system, and the filtering effect is superior to the traditional filtering method.
机译:智能车辆的行驶性能安全稳定,可以显着提高道路交通效率,降低能耗,智能车辆也是现代交通的发展方向。它的核心技术是智能环境感知模块,通过使用汽车上的各种传感器在其中周围环境进行数据收集,为处理模块提供有效的控制奠定基础。本文提出了一种新的SINS / CNS / GPS组合导航观测方程,并为组合导航系统设计了一种新的联合数据融合结构。粒子滤波器用于融合联合滤波器子系统的多源数据,从而消除了传统卡尔曼滤波器的局限性。设计了传统的卡尔曼滤波器结构和联邦粒子滤波机制。比较表明,该算法在组合导航系统的信息融合中是有效的,其滤波效果优于传统的滤波方法。

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